We want to underline that the proposed method can be applied alongside any separation model. In this work, we chose the Sudo rm -rf [1]} architecture since it achieves a good trade-off between speech enhancement quality and time-memory computational requirements. Specifically, we use the Sudo rm -rf variation with group communication [2]} and the default parameters in [3]}, which has shown promising results in speech enhancement tasks [4]}. All models have \(U=8\)  U-ConvBlocks except for the experiments where we increase the depth of the new student networks to 16 and 32. We fix the number of output slots to \(M=3\)  for MixIT models or \(M=2\)  otherwise.
